#adversarial #generative #deeplearning

Deep Learning 27: (1) Generative Adversarial Network (GAN): Introduction and Back-Propagation

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•24 Feb 2019
Ahlad Kumar
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In this lecture introduction to generative adversarial networks (GANs) is carried out in detail. The primary focus of this lecture is on working and back-propagation process. #adversarial#generative#deeplearning
Great and most precise explanation of deep learning I have seen. Can we have the links of these notebooks?
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The description of generator backprop at 16:55 was very helpful. Thanks.
Thank you so much for this amazing explanation.
Wow, Sir! Thank you for this wonderful video.
The explanation is very clear and simple. Thank you so much for this precious content, Sir! Please keep making more videos.
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WOW!!! so far the first lecture is way better than any online resource I've seen!!! thanks for sharing!!!
Thank you sir for such a wonderful playlist . :)
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Thank u soooo much!!! I was eagerly waiting for this for my research. You have seriously come in to my life as an angel at this time as i have been struggling a lot with my research topic and i dont wana get fail😪.
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Thank you for the wonderful video. I was a bit of confused, how the generator generates the image ? suppose in text to image synthesis we have a text which is encoded and joint with noise vector and they say the joint vector is passed to generator and image is generated . But this is being a black box to me ? What's the mechanism to convert the vectored sentence to image . Does the encoder does all by it'self ?
please make videos on deep dream and neural style transfer.
Thaaaank you!! really nice illustration of how GANs work !!
can we have subtitles?
thak you simply the best
Sir..Your videos are fabulous...I have one doubt which is itching my brain a lot..Can you pleasee explain what is the random variable in terms of deep learning
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In my research i need to explain math behind Gan method and implementation of Gan method and any working application of Gan method.i trust that ill get the needed knowledge from your videos...thanks a ton!!
Can i apply this model for Time Series Data or stock price prediction
as close as possible to......D
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Great videos . Please upload videos for CNN
Great video! Couple of questions: 1: What are we achieving by making Generator generate D' close to D or by creating fake images if we can classify images correctly just using Discriminator. 2: How we get value 0.5 and not 0 or 1 after training both G and D. After training what would I expect as y for true image and for fake image. thanks!
Very good explanation of GAN.